Summary of Mtmamba: Enhancing Multi-task Dense Scene Understanding by Mamba-based Decoders, By Baijiong Lin et al.
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
by Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, Ying-Cong Chen
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel architecture for multi-task dense scene understanding, dubbed MTMamba, is proposed in this paper. The model learns to predict multiple dense tasks simultaneously and leverages the Mamba algorithm to handle long-range dependencies and facilitate cross-task interactions. Two core blocks, self-task Mamba (STM) and cross-task Mamba (CTM), are designed to tackle these challenges. Experiments on NYUDv2 and PASCAL-Context datasets demonstrate MTMamba’s superiority over Transformer-based and CNN-based methods. Notably, it achieves significant improvements in tasks like semantic segmentation, human parsing, and object boundary detection on the PASCAL-Context dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MTMamba is a new way to understand scenes by doing multiple things at once. It uses a special technique called Mamba to help it remember what’s happening far away and share information between different tasks. The model has two main parts: one that helps with distant relationships and another that brings tasks together. This paper shows that MTMamba works better than other methods on two important datasets. |
Keywords
» Artificial intelligence » Cnn » Multi task » Parsing » Scene understanding » Semantic segmentation » Transformer
